u

code/paddle-ocr-demo/test.py

@@ -0,0 +1,83 @@
+# from paddleocr import PaddleOCR
+# # 初始化 PaddleOCR 实例
+# ocr = PaddleOCR(
+#     use_doc_orientation_classify=False,
+#     use_doc_unwarping=False,
+#     use_textline_orientation=False)
+# # 对示例图像执行 OCR 推理 
+# result = ocr.predict(
+#     input="test1.png")
+# # 可视化结果并保存 json 结果
+# for res in result:
+#     res.print()
+#     res.save_to_img("output")
+#     res.save_to_json("output")
+
+
+
+from paddleocr import PPChatOCRv4Doc
+
+chat_bot_config = {
+    "module_name": "chat_bot",
+    "model_name": "ernie-3.5-8k",
+    "base_url": "https://qianfan.baidubce.com/v2",
+    "api_type": "openai",
+    "api_key": "bce-v3/ALTAK-5bKjjmEZxz7bnIjpWxxZT/723849a3548dc0d9f639b0ffaed5255a1ccf3f30",  # your api_key
+}
+
+retriever_config = {
+    "module_name": "retriever",
+    "model_name": "embedding-v1",
+    "base_url": "https://qianfan.baidubce.com/v2",
+    "api_type": "qianfan",
+    "api_key": "bce-v3/ALTAK-5bKjjmEZxz7bnIjpWxxZT/723849a3548dc0d9f639b0ffaed5255a1ccf3f30",  # your api_key
+}
+
+pipeline = PPChatOCRv4Doc(
+    use_doc_orientation_classify=False,
+    use_doc_unwarping=False
+)
+
+visual_predict_res = pipeline.visual_predict(
+    input="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_certificate-1.png",
+    use_common_ocr=True,
+    use_seal_recognition=True,
+    use_table_recognition=True,
+)
+
+mllm_predict_info = None
+use_mllm = False
+# 如果使用多模态大模型，需要启动本地 mllm 服务，可以参考文档：https://github.com/PaddlePaddle/PaddleX/blob/release/3.0/docs/pipeline_usage/tutorials/vlm_pipelines/doc_understanding.md 进行部署，并更新 mllm_chat_bot_config 配置。
+if use_mllm:
+    mllm_chat_bot_config = {
+        "module_name": "chat_bot",
+        "model_name": "PP-DocBee",
+        "base_url": "http://127.0.0.1:8080/",  # your local mllm service url
+        "api_type": "openai",
+        "api_key": "api_key",  # your api_key
+    }
+
+    mllm_predict_res = pipeline.mllm_pred(
+        input="test1.png",
+        key_list=["Gross Weight"],
+        mllm_chat_bot_config=mllm_chat_bot_config,
+    )
+    mllm_predict_info = mllm_predict_res["mllm_res"]
+
+visual_info_list = []
+for res in visual_predict_res:
+    visual_info_list.append(res["visual_info"])
+    layout_parsing_result = res["layout_parsing_result"]
+
+vector_info = pipeline.build_vector(
+    visual_info_list, flag_save_bytes_vector=True, retriever_config=retriever_config
+)
+chat_result = pipeline.chat(
+    key_list=["Gross Weight"],
+    visual_info=visual_info_list,
+    vector_info=vector_info,
+    mllm_predict_info=mllm_predict_info,
+    chat_bot_config=chat_bot_config,
+    retriever_config=retriever_config,
+)
+print(chat_result)